Paper from CHI 2008 Proceedings - Improved Video Navigation and Capture.
It present a method for browsing videos by directly dragging their content, in contrast with traditional seeker bar which focus on visual content rather than time.
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Video Browsing By Direct Manipulation - Draft 1
1. Video Browsing by Direct
Manipulation
Pierre Dragicevic, Gonzato Ramos, Jacobo Bibliowicz,
Derek Nowrouzezahrai, Ravin Balakrishman,
Karan Singh
User Interface Design 646
Presented by Vashira Ravipanich
5171439021
2. Introduction
• All video players use
“seeker bar” to control
user interaction
• What if you can directly
dragging in the movie?
3. Introduction
• This paper presents a method for browsing
videos by “directly dragging” their content
• Automatically extracting motion data
• Relative Flow Dragging
4.
5. Why Direct Manipulation?
• Input ~ Output
• Time V.S. Space
• Both are complementary NOT rival
Input like finger move = Output like
mouse movement
Time = seeker Bar, Space = Direct
Manipulation
6. How does it works?
• Videos = sequence of multiple pictures
(frame)
• Extract object(s) movement Call “Trajectory
Extraction”
• Construct “hint path”
7. Relative Flow Dragging
• Directness Directness => user input lang ==
generated output
• Matching gesture with motion
2D = map
3D = scaling object, rotating object
8. Type of dragging
• Curvilinear Dragging
• Flow Dragging
• Relative Dragging
12. Trajectory Extraction
• Computer Vision Approaches
• Object Tracking
- object on video sequence
- motion capture, surveillance
• Optical Flow
- whole picture, calculate pixels
- video compression
• Optical Flow is better for general video player
14. Proposed Solutions
• 3D Distance Method
• (x, y, z) where z is arc-length distance from
the curve origin
15. Limitations
• Video with back-and-forth movement, i.e a
couple dancing tango
• DIfficult to visualize path clearly
16. Evaluation
• User Study
• 6 males, 10 females
• 18 - 44 years old
• Test with 2 videos with given objectives
• Offer both seeker bar and relative flow
dragging
• Which one user comfortable with the most?
20. Previous work on Video Browsing
• Non-Linear Video Browsing
- Segment of difference importance
- Estimating motion activity
• Visual Summaries
- Generate mosaic from key frames
• Content-Based Video Retrieval
21. Conclusion & Future Work
• New way of browsing videos using direct
manipulation
• Appealing to touch-input handheld. iPhone,
Pocket PC.
• Interactive Learning Environments.
22. References
1. Accot, J. and Zhai, S. (1997). Beyond Fitts' law: mod- 11. Dragicevic, P., Huot, S. and Huot, S. (2002). SpiraC-
els for trajectory-based HCI tasks. CHI. p. 295-302. lock: a continuous and non-intrusive display for up-
2. Appert, C. and Fekete, J. (2006). OrthoZoom scroller: coming events. CHI Extended Abstracts. p. 604-605.
1D Multi-Scale Navigation. CHI. P. 21-30. 12. Goldman, D.B., Curless, B., Salesin, D. and Seitz, S.M.
3. Autodesk Maya. http://www.autodesk.com/ (2006). Schematic storyboarding for video visualization
4. Baudel, T., Fitzmaurice, G., Buxton, W., Kurtenbach, and editing. SIGGRAPH. p. 862-871.
G., Tappen, C. and Liepa, P. (2002). Drawing system 13. Guimbretière, F. (2000). FlowMenu: combining com-
using design guides. US Patent # 6,377,240. mand, text, and data entry. UIST. p. 213-216.
5. Beauchemin, S.S. and Barron, J.L. (1995). The compu- 14. Hölzl, R. (1996). How does ‘dragging’ affect the learn-
tation of optical flow. ACM Computing Surveys, 27(3). ing of geometry? International Journal of Computers
p. 433-467. for Mathematical Learning, 1(2). p. 169-187.
6. Beaudouin-Lafon, M. (2000). Instrumental Interaction: 15. Hutchins, E.L., Hollan, J.D. and Norman, D.A. (1987).
An interaction model for designing post-WIMP user in- Direct manipulation interfaces. In Human-Computer in-
terfaces. CHI. p. 446-453. teraction: A Multidisciplinary Approach. R. M. Baeck-
7. Beaudouin-Lafon, M. (2001). Novel interaction tech- er, Ed. Morgan Kaufmann. p. 468-470.
niques for overlapping windows. UIST. p. 153-154. 16. Irani, M., Anadan, P. and Hsu, H. (1995). Mosaic based
8. Bezerianos, A., Dragicevic, P. and Balakrishnan, R. representations of video sequences and their applica-
(2006). Mnemonic rendering: an image-based approach tions. Intl. Conference on Computer Vision. p. 605-611.
for exposing hidden changes in dynamic displays. 17. Kim, C. and Hwang, J. (2002). Fast and automatic
UIST. p. 159-168. video object segmentation and tracking for content-
9. Buxton, W. (1986). There's more to interaction than based applications. IEEE Trans. Circuits and Systems
meets the eye: some issues in manual input. In User for Video Technology, 12. p. 122-129.
Centered System Design: New Perspectives on Human- 18. Kimber D., Dunnigan, T., Girgensohn, A., Shipman, F.,
Computer Interaction. Lawrence Erlbaum. p. 19-337. Turner, T. and Yang, T. (2007). Trailblazing: Video
10. Cheng,Y. (1995). Mean shift, mode seeking, and clus- playback control by direct object manipulation. ICME.
tering. IEEE Transactions on Pattern Analysis and Ma- p. 1015-1018.
chine Intelligence, 17(8). p. 790-799. 19. Li, F.C., Gupta, A., Sanocki, E., He, L. and Rui, Y.